Mathematical programming models for joint simulation–optimization applied to closed queueing networks

Annals of Operations Research

Volumn 231, Issue 1, 2015, Pages 105-127

  Alfieri, Arianna ^a   Matta, Andrea ^b   Pedrielli, Giulia ^b  

^a POLITECNICO DI TORINO   (Italy)

^b POLITECNICO DI MILANO   (Italy)

Author keywords

Discrete event systems; Loop manufacturing systems; Mathematical programming; Simulation optimization

Indexed keywords

EID: 84938395751     PISSN: 02545330     EISSN: 15729338     Source Type: Journal    
DOI: 10.1007/s10479-013-1480-7     Document Type: Article

References (32)

1. Alfieri, A., & Matta, A. (2012). Mathematical programming formulations for approximate simulation of multistage production systems. European Journal of Operational Research. doi:10.1016/j.ejor.2011.12.044.
2. Birge, J., & Louveaux, F. (1997). Introduction to stochastic programming. Springer series in operations research and financial engineering series. Berlin: Springer.
3. Boesel, J. N., Barry, L., & Kim, S. (2003). Using ranking and selection to “clean up” after simulation optimization. Operations Research, 51, 814–825. doi:10.1287/opre.51.5.814.16751.
4. Bouhchouch, A., Frein, Y., & Dallery, Y. (1993). Analysis of a closed-loop manufacturing system with finite buffers. Applied Stochastic Models and Data Analysis, 9(2), 111–125. doi:10.1002/asm.3150090205
5. Brodsky, A., Pedersen, J., & Wagner, A. (2003). On the complexity of buffer allocation in message passing systems. Journal of Parallel and Distributed Computing, 65(6), 692–713.
6. Chan, W., & Schruben, L. (2008a). Optimization models of discrete–event system dynamics. Operations Research, 56(5), 1218–1237.
7. Chan, W. K., & Schruben, L. W. (2008b). Mathematical programming models of closed tandem queueing networks. ACM Transactions on Modeling and Computer Simulation, 19(1). doi:10.1016/j.jpdc.2004.10.009.
8. Chick, S., Schmeiser, B., Sánchez, P. J., Ferrin, D., Morrice, D. J., & Jin, J. (2003). Simulation-based retrospective optimization of stochastic systems: a family of algorithms. In Proceedings of the 2003 winter: Vol. 1. Simulation conference, 2003 (pp. 543–547).
9. Dallery, Y., & Frein, Y. (1989). A decomposition method for approximate analysis of closed queueing networks with blocking. In Queueing networks with blocking (pp. 193–216).
10. Dallery, Y., & Liberopoulos, G. (2000). Extended kanban control system: combining kanban and base stock. IIE Transactions, 32, 369–386.
11. Dolgui, A., Eremeev, A., & Sygaev, V. (2010). A problem of buffer allocation in production lines: complexity analysis and algorithms. In 3rd international conference on metaheuristics and nature inspired computing, Djerba, Tunisia.
12. Fu, M., Glover, F., & April, J. (2005). In Simulation optimization: a review, new developments, and applications (pp. 83–95).
13. Garey, M., & Johnson, D. (1979). Computers and intractability: a guide to the theory of NP-completeness. New York: Freeman
14. Gershwin, S. B., & Werner, L. (2007). An approximate analytical method for evaluating the performance of closed-loop flow systems with unreliable machines and finite buffers. International Journal of Production Research, 45, 3085–3111.
15. Healy, K., & Schruben, L. W. (1991). Retrospective simulation response optimization. In Proceedings of the 23rd conference on winter simulation, WSC ’91 (pp. 901–906). Washington: IEEE Computer Society.
16. Higle, J., & Sen, S. (1996). Stochastic decomposition: a statistical method for large scale stochastic linear programming. Nonconvex optimization and its applications. Norwell: Kluwer Academic.
17. Hong, L. J., & Nelson, B. L. (2006). Discrete optimization via simulation using compass. Operations Research, 54(1), 115–129.
18. Kleijnen, J. P. C. (2008). Design and analysis of simulation experiments. International series in operations research & management science: Vol. 111. Berlin: Springer.
19. Kushner, H., & Yin, G. (1997). Stochastic approximation algorithms and applications. Berlin: Springer.
20. Law, A. (2007). Simulation modeling and analysis (4th ed.). New York: McGraw-Hill.
21. Maggio, N., Matta, A., Gershwin, S., & Tolio, T. (2009a). A decomposition approximation for three-machine closed-loop production systems with unreliable machines, finite buffers and a fixed populatio. IIE Transactions, 41(6), 562–574.
22. Maggio, N., Matta, A., Gershwin, S. B., & Tolio, T. (2009b). A decomposition approximation for three-machine closed-loop production systems with unreliable machines, finite buffers and a fixed population. IIE Transactions, 41(6), 562–574.
23. Matta, A. (2008). Simulation optimization with mathematical programming representation of discrete event systems. In S. J. Mason, R. R. Hill, L. Monch, O. Rose, T. Jefferson, & J. W. Fowler (Eds.), Proceedings of the 2008 winter simulation conference Piscataway (pp. 1393–1400). New Jersey: Institute of Electrical and Electronics Engineers, Inc.
24. Matta, A., & Chefson, R. (2005). Formal properties of closed flow lines with limited buffer capacities and random processing times. In Proceedings of the European simulation and modelling conference, Porto, Portugal (pp. 190–194).
25. Ming-Guang, H., Pao-Long, C., & Ying-Chyi, C. (2002). Buffer allocation in flow-shop-type production systems with general arrival and service patterns. Computers & Operations Research, 29(2), 103–121.
26. Montgomery, D. (2005). Progettazione e analisi degli esperimenti. Istruzione scientifica. New York: McGraw-Hill.
27. Myers, R., Montgomery, D., & Anderson-Cook, C. (2009). Response surface methodology: process and product optimization using designed experiments. Wiley series in probability and statistics. New York: Wiley.
28. Pedrielli, G. (2013). Discrete event systems simulation–optimization: time buffer framework. PhD thesis, Mechanical Engineering Department, Politecnico di Milano, Italy.
29. Robinson, S. (1996). Analysis of sample–path optimization. Mathematics of Operations Research, 21, 513–528.
30. Schruben, L. W. (2000). Mathematical programming models of discrete event system dynamics. In J. A. Joines, R. R. Bartona, K. Kang, & P. A. Fishwick (Eds.), Proceedings of the 2000 winter simulation conference Piscataway (pp. 381–385). New Jersey: Institute of Electrical and Electronics Engineers, Inc.
31. Spall, J. C. (2003). Introduction to stochastic search and optimization. New York: Wiley.
32. Zhang, Z. (2006). Analysis and design of manufacturing systems with multiple-loop structures. PhD thesis, Mechanical Engineering Department, Massachussetts Institute of Technology, Berkeley.